The dynamics of the tripartite relationship between the host, gut bacteria and diet in the gut is relatively unknown. An imbalance between harmful and protective gut bacteria, termed dysbiosis, has been linked to many diseases and has most often been attributed to high-fat dietary intake. However, we recently clarified that the type of fat, not calories, were important in the development of murine colitis. To further understand the host-microbe dynamic in response to dietary lipids, we fed mice isocaloric high-fat diets containing either milk fat, corn oil or olive oil and performed 16S rRNA gene sequencing of the colon microbiome and mass spectrometry-based relative quantification of the colonic metaproteome. The corn oil diet, rich in omega-6 polyunsaturated fatty acids, increased the potential for pathobiont survival and invasion in an inflamed, oxidized and damaged gut while saturated fatty acids promoted compensatory inflammatory responses involved in tissue healing. We conclude that various lipids uniquely alter the host-microbe interaction in the gut. While high-fat consumption has a distinct impact on the gut microbiota, the type of fatty acids alters the relative microbial abundances and predicted functions. These results support that the type of fat are key to understanding the biological effects of high-fat diets on gut health.

Accurate tumor classification is essential for guiding treatment, yet histology alone may overlook key molecular differences or result in misclassification. We present a multimodal strategy that integrates gene expression (mRNA) and DNA methylation data to improve classification accuracy and detect misclassified tumors. Using 6216 samples from The Cancer Genome Atlas (TCGA), we applied Support Vector Machines (SVMs) and hierarchical clustering to evaluate classification accuracy across single and integrated platforms. mRNA and methylation data alone achieved accuracies of 97% and 95.4%, respectively. Their integration further reduced false positives and improved the identification of outliers, including histologically misclassified cases such as papillary renal cell carcinoma samples clustering with bladder cancer. The integrated approach also revealed molecular subtypes correlated with somatic mutations and patient survival, offering clinically relevant insights. Our findings highlight the value of combining genetic and epigenetic profiles to refine cancer diagnostics. This framework enhances diagnostic precision, supports treatment decisions, and provides a scalable quality control tool for molecular oncology.